ArticleScientific reports2025
Machine learning ensemble technique for exploring soil type evolution.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- A novel approach to soil nutrients prediction model for Bezuidenhout Park, Johannesburg, Gauteng Province, South Africa: attention temporal neural networks (ATNN).Scientific reports · 2026Article
- Pervasive seasonal divergence in lagged responses of vegetation growth to compound drought-heat stress on the Tibetan Plateau.Frontiers in plant science · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
Machine learning has shown great potential in predicting soil properties, but individual models are often prone to overfitting, limiting their generalization. Ensemble models address this challenge by combining the strengths of multiple algorithms. This study applies a voting-based ensemble model (VEM), integrating Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), to gain a deeper understanding of soil type evolution, which is crucial for land management and soil conservation. The research, conducted in the Tongzhou District of Beijing, uses 5,000 sampling points selected via genetic algorithms for model training, 237 surface samples for consistency testing, and 97 profiles for field validation. The VEM demonstrates high accuracy and robustness, producing a detailed soil type map and identifying key trends in soil type evolution. This study highlights the effectiveness of ensemble models in understanding soil evolution and offers valuable insights into soil system dynamics.
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Registered trials
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